During our experiments, the systems under evaluation showed a sufficient degree of generalization over unseen audio signals when processed using the same distortion algorithms. Nevertheless, their performance drops significantly when processing real-world distorted guitar sounds, as we limited our experiments to two simplified methods for distortion, which represent only part of a commercial distortion effect. The positive outcomes of our experiments suggest that the neural models in this work can potentially be used to remove more complex distortion effects, if the training data includes such complex distortion algorithms (or their high-quality emulation, e.g., by using the frameworks [1]} or [2]}). We plan to validate this hypothesis in future work.
